IntelligentCrowd：基于多智能体强化学习的移动人群感知
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning
摘要：
智能移动设备的繁荣使得移动人群感知（MCS）成为完成复杂感知和计算任务的一种有前途的范例。在过去的研究中，人们从MCS平台的角度出发，在激励机制和任务分配策略的设计上做了大量的工作，以激励移动用户的参与。然而，在实践中，MCS参与者面临着来自其感知环境以及其他参与者策略的许多不确定性，并且他们如何相互交互并做出感知决策还没有得到很好的理解。在本文中，我们采取MCS参与者的角度来推导一个在线感知策略，以最大限度地提高他们的收益，通过MCS的参与。具体来说，我们的移动用户和传感环境的交互模型作为一个多代理马尔可夫决策过程。每个参与者不能观察其他人的决定，而是需要仅基于本地信息来决定她在感知任务中的努力水平，例如，它自己的感知信号质量记录。为了应对随机感知环境，我们开发了一个智能人群感知算法智能人群利用多智能体强化学习（MARL）的力量。我们的算法导致每个用户的最佳感知策略，以最大限度地提高对随机感知环境的预期回报，并可以在个人参与者的水平，以分布式的方式实现。数值模拟表明，IntelligentCrowd显着提高用户的支付顺序MCS任务在各种传感动态。
英文摘要：
The prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks. In the past, great efforts have been made on the design of incentive mechanisms and task allocation strategies from MCS platform's perspective to motivate mobile users' participation. However, in practice, MCS participants face many uncertainties coming from their sensing environment as well as other participants' strategies, and how do they interact with each other and make sensing decisions is not well understood. In this paper, we take MCS participants' perspective to derive an online sensing policy to maximize their payoffs via MCS participation. Specifically, we model the interactions of mobile users and sensing environments as a multi-agent Markov decision process. Each participant cannot observe others' decisions, but needs to decide her effort level in sensing tasks only based on local information, e.g., its own record of sensed signals' quality. To cope with the stochastic sensing environment, we develop an intelligent crowdsensing algorithm IntelligentCrowd by leveraging the power of multi-agent reinforcement learning (MARL). Our algorithm leads to the optimal sensing policy for each user to maximize the expected payoff against stochastic sensing environments, and can be implemented at individual participant's level in a distributed fashion. Numerical simulations demonstrate that IntelligentCrowd significantly improves users' payoffs in sequential MCS tasks under various sensing dynamics.
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